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2nd International Conference on Cyber-Energy systems and intelligent Energy, ICCSIE 2024

作者： Lv, Changyao Wang, Yaqi Chen, Yudong Yu, Zhuo Hu, Guoliang Wu, Hongliang Li, Shuai Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai201324 China

ISBN: (纸本)9798350373318

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga Whale Optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes. © 2024 IEEE.

关键词： Anodic oxidation

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A Haptic Exploration and Surface Classification of Objects with Four Typical Surface Properties 8

A Haptic Exploration and Surface Classification of Objects w...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Qi, Peng Wu, Yunfeng Yao, Tianliang Lu, Bo Sun, Yi Dai, Jian S. College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Jiangsu Suzhou China Ecole Polytechnique Federale de Lausanne Laboratory of Intelligent Systems Lausanne1015 Switzerland College of Engineering Southern University of Science and Technology Department of Mechanical and Energy Engineering Shenzhen518055 China School of Natural Mathematical and Engineering Sciences King's College London United Kingdom

ISBN: (纸本)9798350300178

To effectively interact with the physical world, an intelligent robot is required to have the ability to obtain the detailed features of an unknown object. Visual devices are commonly used to detect the global geometry of an object;however, detailed information such as surface properties cannot be identified using these devices. The current study proposes an efficient haptic exploration method to recognize the physical properties of surfaces using an intelligent fingertip. Our surface-following algorithm utilizes the normal force vector and the tangential force vector at the contact point between the fingertip and the target object to predict the moving direction and implement the surface exploration. And a correction index K is introduced to adjust the sliding velocity. The algorithm is proved to be robust and high performance both in simulation and lab experiments. Finally, we propose and explore a haptic prediction neural network, which enables our robot to have an accurate feel through physical interaction. In the classification experiment of four types of objects with different surface properties, the average accuracy of the proposed model is 90.2%. © 2023 IEEE.

关键词： Surface properties

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Data Cleaning Based on Multi-sensor Spatiotemporal Correlation 4th

Data Cleaning Based on Multi-sensor Spatiotemporal Correlati...

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4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Shao, Baozhu Song, Chunhe Wang, Zhongfeng Li, Zhexi Yu, Shimao Zeng, Peng Liaoning Electric Power Research Institute State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Shenyang Power Supply Company State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China

ISBN: (纸本)9783030323875

Sensor-based condition monitoring systems are becoming an important part of modern industry. However, the data collected from sensor nodes are usually unreliable and inaccurate. It is very critical to clean the sensor data before using them to detect actual events occurred in the physical world. Popular data cleaning methods, such as moving average and stacked denoise autoencoder, cannot meet the requirements of accuracy, energy efficiency or computation limitation in many sensor related applications. In this paper, we propose a data cleaning method based on multi-sensor spatiotemporal correlation. Specifically, we find out and repair the abnormal data according to the correlation of sensor data in adjacent time and adjacent space. Real data based simulation shows the effectiveness of our proposed method. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Sensor nodes

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Weld Defects Detection by Neural Networks Based on Attention Mechanism

Weld Defects Detection by Neural Networks Based on Attention...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Guang Yang Zhifeng Liu Jianjun Wang Shuping Cui State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Welding technology plays an indispensable role in ship construction, and its maturity has a direct impact on the quality of shipbuilding. The weld seam, being the weakest part of a welded structure, significantly affects the structural strength, service life, and safety reliability of ships. However, the current identification of weld defects in the ship industry relies on manual methods, which are subjective and prone to error. To address this issue, this paper presents a novel approach by collecting weld images from actual ship scenes, preprocessing them to eliminate noise and highlight defect features, and proposing a Transformer-based improved target detection model (T-OBJDETC) to enhance the recognition accuracy of the model.

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A History Information-Based A* Algorithm for the Path Planning with Multiple Alternative Destinations

A History Information-Based A* Algorithm for the Path Planni...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ke Wang Wei Liang Huaguang Shi Jialin Zhang Qi Wang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

As a cornerstone technology in automation, path planning plays a crucial role across various domains, including logistics, autonomous vehicles, and robotics. A novel path planning challenge, focusing on routes with multiple alternative destinations, has emerged as a focal point in industrial settings. Traditional approaches such as breadth-first and heuristic searches lack specific optimizations for this challenge, resulting in suboptimal efficiency. This paper introduces a new approach to the path planning problem with multiple alternative destinations that utilizes historical data. It features three heuristic strategies: leveraging historical information for search acceleration, pruning based on the length of the current shortest path, and searching in ascending order of the Manhattan distance. These strategies enhance the search efficiency by leveraging historical information for faster searches and pruning based on real-time search outcomes. Simulation results show that the proposed algorithm outperforms the existing works in terms of the computational cost.

关键词： Service robots Navigation Simulation Focusing Path planning Real-time systems Computational efficiency

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Actor-Based Learning Framework for the Co-Optimization of Modular Cpps Scheduling and control with Stochastic Processing Times

SSRN

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SSRN 2023年

作者： Wan, Guangxi Dong, Xiaoting Dong, Qingwei He, Yunpeng Zeng, Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

Scheduling with stochastic processing times is a kind of dynamic scheduling in production systems. The current scheduling optimization problem is formulated in the scheduling domain, which ignores the underlying synergies for optimization objectives between scheduling and control. To address this problem, we propose and formulate the problem of the co-optimization of scheduling and control with stochastic processing times (COSC-SPT) with a system respective view. To solve the COSC-SPT, we model the decision-making process as a Markov decision process and propose an actor-based learning framework to facilitate problem solving. To verify the feasibility of the proposed framework and the effectiveness of the formulated COSC-SPT, a real reducer assembly system is taken as a use case. The results of experiments demonstrate that the COSC-SPT provides better solutions than conventional scheduling problems. Keywords: Actor, cyber-physical production systems, optimization, scheduling, stochastic processing time. © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Low-Power Heterogeneous Networking Method Based on NB-IoT and WSN

Low-Power Heterogeneous Networking Method Based on NB-IoT an...

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International Conference on Communication Engineering and Technology (ICCET)

作者： Mo Guan Liangliang Qi Xi Jin Suoling Guan Changqing Xia Chi Xu Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

Digital oilfields generally adopt the multi-hop technology to expand the network scale, and the data generated depends on the large-scale wireless sensor network (WSN) for transmission. However, multi-hop communication will lead to uneven distribution of network energy consumption and premature death of nodes. To prolong the network lifetime of digital oilfields, we extended the narrowband internet of things (NB-IoT) module on some WSN nodes deployed on site to form heterogeneous nodes. Through the heterogeneity of NB-IoT and WSN, the nodes could transmit as little data as possible from other nodes, fundamentally reducing the node energy consumption. Furthermore, an optimization method was proposed that could simultaneously optimize the network lifetime and node expansion cost. The experimental results revealed that the proposed algorithm could greatly prolong the network lifetime while only introducing a small number of nodes.

关键词： Wireless sensor networks Energy consumption Costs Optimization methods Spread spectrum communication Internet of Things Narrowband

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Micro Channel for Analyzing Mechanical Adaption of Cancer Cell

Micro Channel for Analyzing Mechanical Adaption of Cancer Ce...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Pengyun Li Xiaoming Liu Xiaoqing Tang Masaru Kojima Jian Huang Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and the Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Osaka University 1-3 Machikaneyama Toyonaka Osaka Japan Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

It was reported that cancer cells could biologically respond and adapt to the confined environment during migration in extracellular matrix (ECM). But it is not clearly studied that how cancer cells respond and adapt to confined environment in mechanical way. Thus, in this study, we designed and fabricated a kind of one-layer micro channel to mimic confined environment for analyzing mechanical adaption of cell. This micro channel could apply sequential mechanical stresses on a number of cells with high throughput, and the time of cells passing through five rows of micro channels were used to evaluate their adaptability to the sequential and confined space. In experiments, each cell suffered 20 times of mechanical stresses after passing through 20 squeezing channels arranged in 5 rows. We observed the sequential squeezing processes under microscope and investigated the passing time using a high-speed camera. The preliminary results indicated that cancer cells could pass through and adapt to the confined environment faster than normal cells.

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Research on fault feature extraction of rotating machinery based on adaptive VMD and optimized CYCBD

Research on fault feature extraction of rotating machinery b...

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第34届中国控制与决策会议

作者： Ran Xie Ling Xiong Binbin Dan Zeyu Ren Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control of Ministry of Education School of Physics and Electronic Science Hunan Institute of Science and Technology

To solve the problem that early fault features of rotating machinery are difficult to extract,an adaptive k-value hierarchical variational mode decomposition(H-VMD) combined with optimized maximum second-order cyclostationarity blind deconvolution(CYCBD) fault feature extraction method is proposed in this *** decomposition of the vibration signal is performed with *** then the noise dominant component is denoised by wavelet threshold denoising(WTD).Furthermore,the improved autocorrelation-weighted correlated kurtosis(ACK)when CYCBD enhances the periodic shock component of the denoised signal is the fitness function of ChOA,and the envelope demodulation analysis of the feature-enhanced signal is performed using the teager energy operator(TEO).Simulation analysis and experimental results show that the interference of background noise can be effectively removed and the periodic shock component of the vibration signal be enhanced by the proposed method,which is a new feature extraction method for the fault diagnosis of rotating machinery.

关键词： Variational mode decomposition Maximum second-order cyclostationarity blind deconvolution Wavelet threshold denoising Teager energy operator Fault feature extraction

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An Anomaly Detection Method of Industrial Data Based on Stacking Integration

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Journal on Artificial Intelligence 2021年第1期3卷 9-19页

作者： Kunkun Wang Xianda Liu College of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang110159China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China

With the development of Internet technology,the computing power of data has increased,and the development of machine learning has become faster and *** the industrial production of industrial control systems,quality inspection and safety production of process products have always been our *** at the low accuracy of anomaly detection in process data in industrial control system,this paper proposes an anomaly detection method based on stacking integration using the machine learning *** are collected from the industrial site and processed by feature *** component analysis(PCA)and integrated rule tree method are adopted to reduce the dimension of the process data,which can restore the original feature information of the data to the maximum *** forest(RF),Adaboost,XGboost,SVM were selected as the first layer of basic *** regression(LR)was used as the secondary learner to build the exception detection model based on stacking integrated *** data was used to train the base learner model and the integrated *** comparing and analyzing the experimental results of between integrated model and each basic learning *** comparing and analyzing the experimental results of the constructed anomaly detection model and the basic learning model,the accuracy of process data anomaly detection is effectively improved,and the false alarm rate of process data anomaly detection is effectively reduced.

关键词： Industrial control system anomaly detection random forest SVM stacking

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